A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis

Authors:
Yang Yang;Yinxia Liao;Guang Meng;Jay Lee
Affiliations:
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240, PR China;NSF I/UCR Center for Intelligent Maintenance Systems, 560 Rhodes Hall, University of Cincinnati, Cincinnati, OH 45221, USA;State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240, PR China;NSF I/UCR Center for Intelligent Maintenance Systems, 560 Rhodes Hall, University of Cincinnati, Cincinnati, OH 45221, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

With the development of the condition-based maintenance techniques and the consequent requirement for good machine learning methods, new challenges arise in unsupervised learning. In the real-world situations, due to the relevant features that could exhibit the real machine condition are often unknown as priori, condition monitoring systems based on unimportant features, e.g. noise, might suffer high false-alarm rates, especially when the characteristics of failures are costly or difficult to learn. Therefore, it is important to select the most representative features for unsupervised learning in fault diagnostics. In this paper, a hybrid feature selection scheme (HFS) for unsupervised learning is proposed to improve the robustness and the accuracy of fault diagnostics. It provides a general framework of the feature selection based on significance evaluation and similarity measurement with respect to the multiple clustering solutions. The effectiveness of the proposed HFS method is demonstrated by a bearing fault diagnostics application and comparison with other features selection methods.